For the estimated 200,000–400,000 athletes who sustain ACL tears annually in the United States alone, the prospect of AI-driven early warning and smarter return-to-sport decisions represents a meaningful clinical opportunity. This scoping review synthesizes a decade of emerging evidence and reveals where machine learning genuinely adds value — and where it still falls short of clinical readiness.
Drawing on 40 peer-reviewed studies published between 2016 and 2025 across four major databases, the review finds that tree-based ensemble algorithms — specifically Random Forest and Extreme Gradient Boosting — delivered the most consistent predictive performance across clinical, biomechanical, and wearable sensor datasets. Deep learning architectures proved most effective for image-based tasks such as detecting ACL tears and evaluating graft integrity on MRI. Wearable and sensor-integrated pipelines enabled continuous functional monitoring throughout rehabilitation phases. Methodological quality was assessed using TRIPOD-AI, PROBAST-AI, and an adapted GRADE framework, revealing that while model accuracy metrics were often reported, external validation, prospective study designs, and diverse cohorts remain the exception rather than the rule.
The broader context here is critical. ACL injury prediction has long struggled with low base rates — even in high-risk athletic populations, true-positive prediction without catastrophic false-positive rates is statistically brutal. Machine learning does not dissolve this problem; it reframes it. What this review signals is that ML's clearest near-term clinical value may lie not in pre-injury prediction, but in rehabilitation monitoring and return-to-sport gate-keeping, where continuous wearable data streams can detect asymmetries and recovery plateaus invisible to periodic clinical assessments. The systematic absence of prospective, multi-site validation studies is the field's defining limitation. Most models were trained and tested on narrow, homogeneous cohorts — often male athletes from single sports — raising serious generalizability concerns. This is a confirmatory but useful synthesis: it maps a maturing field with real clinical momentum, while honestly flagging that standardization, external validation, and health equity in dataset composition must precede deployment at scale.
